People often rely on heuristic reasoning when receiving algorithm advice, and this reliance leads to biased decisions that undermine the effectiveness of human-AI collaboration. Such bias persists even when individuals are given more time to deliberate or provided more information about AI, as they may lack the awareness or ability to engage in systematic reasoning. In this paper, we explore how guided reflection may enhance decision-making performance in human-AI collaboration by prompting a systematic reasoning process. We conducted an experiment with 178 participants, comparing decision-making behavior across three conditions: AI, explainable AI (XAI), and XAI with reflection. The results demonstrate that reflection significantly reduced over-reliance on AI and improved decision accuracy. Individuals with a high need for cognition and a high perceived understanding of AI benefited more from reflection. Furthermore, our study uncovers distinct patterns of cognitive processing and belief adjustment across different experimental conditions. Our findings provide a practical strategy for fostering cognitive engagement and contribute to a deeper understanding of human cognitive processes in AI-assisted decision-making.

MOTIVATION: The development of single-cell RNA sequencing (scRNA-seq) technology makes it possible to study the cellular dynamic processes such as cell cycle and cell differentiation. Due to the difficulties in generating genuine time-series scRNA-seq data, it is of great importance to computationally infer the pseudotime of the cells along differentiation trajectory based on their gene expression patterns. The existing pseudotime prediction methods often suffer from the high level noise of single-cell data, thus it is still necessary to study the single-cell trajectory inference methods. RESULTS: In this study, we propose a branched local tangent space alignment (BLTSA) method to infer single-cell pseudotime for multi-furcation trajectories. By assuming that single cells are sampled from a low-dimensional self-intersecting manifold, BLTSA first identifies the tip and branching cells in the trajectory based on cells' local Euclidean neighborhoods. Local coordinates within the tangent spaces are then determined by each cell's local neighborhood after clustering all the cells to different branches iteratively. The global coordinates for all the single cells are finally obtained by aligning the local coordinates based on the tangent spaces. We evaluate the performance of BLTSA on four simulation datasets and five real datasets. The experimental results show that BLTSA has obvious advantages over other comparison methods. AVAILABILITY AND IMPLEMENTATION: R codes are available at https://github.com/LiminLi-xjtu/BLTSA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.